Quantitative proteomics reveals pregnancy prognosis signature of polycystic ovary syndrome women based on machine learning

Quantitative proteomics reveals pregnancy prognosis signature of polycystic ovary syndrome women based on machine learning

Objective We aimed to screen and construct a predictive model for pregnancy loss in polycystic ovary syndrome (PCOS) patients through machine learning methods.Methods We obtained the endometrial samples from 33 PCOS patients and 7 healthy controls at the Reproductive Center of the Second Hospital of...

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Bibliographic Details
Main Authors: Yuanyuan Wu, Cai Liu, Jinge Huang, Fang Wang
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:Gynecological Endocrinology
Subjects:
Polycystic ovary syndrome
prognostic model
quantitative proteomics
machine learning
TIA1
COL5A1
Online Access:https://www.tandfonline.com/doi/10.1080/09513590.2024.2328613
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https://www.tandfonline.com/doi/10.1080/09513590.2024.2328613

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